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FreeCAD LLM Automation - Multi-Agent System Implementation Plan

Executive Summary

This document outlines the 12-week implementation roadmap for transforming the FreeCAD LLM automation system into a production-ready, multi-agent architecture with advanced 3D understanding, real-time collaboration, and industrial compliance.

Target Metrics:

  • Response Time: <5s per design iteration
  • Concurrent Users: 100+
  • Success Rate: >85% on complex CAD tasks
  • FEA Integration: Automated stress/thermal analysis

1. System Architecture Overview

1.1 Core Technology Stack

Component Technology Purpose
Orchestration Docker, Kubernetes Container management, scaling
State Management Redis (TTL Cache), Redis Streams Real-time state, audit trails
CAD Engine FreeCAD (Headless), Pivy 3D modeling execution
Distributed Compute Ray Parallel agent execution
Vector Store Milvus/FAISS Geometric embeddings
Message Bus LangGraph/AutoGen Agent communication
API Gateway FastAPI RESTful interface

1.2 Multi-Agent System (MAS) Design

Agent Specifications

1. Planner Agent

  • Technology: Chain-of-Thought (CoT) reasoning, RAG with ISO/DIN standards
  • Vector DB: Milvus for design pattern retrieval
  • FreeCAD Interaction: Queries DOM tree for state-aware task decomposition
  • Output: Task dependency graph (NetworkX JSON format)

2. Generator Agent

  • Technology: CodeLlama (Fine-tuned with LoRA), Llama3
  • Specialization: PartDesign body workflow (Sketch → Pad → Pocket)
  • FreeCAD API: Direct Python scripting with constraint enforcement
  • Error Handling: Dynamic indexing for Sketcher constraints

3. Validator Agent

  • Technology:
    • Geometric: CalculiX (FEA), Gmsh (meshing)
    • Visual: GPT-4V multimodal critique
    • Geometric Analysis: Open3D for manifold checks
  • FreeCAD Interaction: Exports STL/STEP for external validation
  • Output: Pass/fail + detailed feedback JSON

4. Orchestrator Agent

  • Technology: LangGraph state machine, exponential backoff
  • Responsibility:
    • Recompute monitoring
    • Topological naming error recovery
    • Loop convergence (max 10 iterations)
    • Human-in-the-loop triggers

2. ReAct Design Loop Architecture

2.1 Data Flow Diagram

User Prompt
    ↓
[Planner Agent] → Task Graph JSON
    ↓
[Generator Agent] → FreeCAD Python Script
    ↓
[Headless FreeCAD Execution] → DOM Update
    ↓
[State Encoder]
    ├─→ BERT (FreeCAD XML)
    ├─→ PointNet++ (Mesh geometry)
    └─→ GraphSAGE (B-Rep features)
    ↓
[Vector DB Storage] + [Redis Cache]
    ↓
[Validator Agent]
    ├─→ Geometric (manifold, tolerances)
    ├─→ Physical (FEA simulation)
    └─→ Visual (GPT-4V critique)
    ↓
[Orchestrator Decision]
    ├─→ Success → Export (STEP/IGES)
    ├─→ Minor Issues → Refine (loop)
    └─→ Critical Failure → Human Review

2.2 State Serialization Strategy

Hierarchical DOM Encoding:

{
  "document": {
    "bodies": [
      {
        "id": "Body001",
        "features": [
          {"type": "Sketch", "constraints": [...], "geometry": [...]},
          {"type": "Pad", "length": 50, "direction": "Z"}
        ]
      }
    ],
    "embeddings": {
      "text": [...],  # BERT 768-dim
      "geometry": [...],  # PointNet 1024-dim
      "topology": [...]  # GraphSAGE 256-dim
    }
  }
}

Storage:

  • Hot State: Redis (TTL 1hr)
  • Long-term: Milvus (vector search)
  • Audit Trail: Redis Streams (immutable log)

3. Implementation Roadmap (12 Weeks)

Phase 1: Core Infrastructure & State Management (Weeks 1-2)

Week 1: Headless Stabilization

Objectives:

  • Eliminate GUI dependencies in production
  • Implement robust error handling for topological naming

Tasks:

  1. Xvfb Integration (tools/gui/headless_manager.py)

    - Virtual display wrapper for FreeCAD.Gui
- Fallback to console-only mode
- Process lifecycle management

  2. Recompute Error Handling (core/freecad/document_manager.py)

    - Try-catch wrapper for doc.recompute()
- Exponential backoff (3 attempts)
- State rollback on failure
- Detailed error logging with DOM snapshot

  3. Testing:

    • 50 complex models with circular references
    • Memory leak profiling (target: <100MB growth/hour)

Deliverables:

  • Headless FreeCAD runs in Docker without X11
  • 95% recompute success rate on test dataset
  • CI/CD pipeline with headless tests

Week 2: MAS Foundation & Message Bus

Objectives:

  • Transition from monolithic to multi-agent architecture
  • Implement inter-agent communication

Tasks:

  1. Agent Refactoring (core/agents/)

    agents/
├── planner.py         # CoT + RAG
├── generator.py       # CodeLlama wrapper
├── validator.py       # Multi-modal validation
└── orchestrator.py    # LangGraph state machine

  2. LangGraph Integration

    - Define state schema (TypedDict)
- Implement conditional edges (validation pass/fail)
- Configure message passing protocol

  3. Redis Message Bus (core/messaging/redis_bus.py)

    - Pub/Sub channels per agent
- Request/response pattern with correlation IDs
- Dead letter queue for failures

Deliverables:

  • 4 independent agents with clean interfaces
  • LangGraph workflow executes simple box model
  • Redis integration tests (concurrent agents)

Phase 2: Intelligence Layer & 3D Machine Learning (Weeks 3-6)

Week 3-4: Planner & Generator Enhancement

Planner Agent:

  1. RAG Implementation (agents/planner/rag_engine.py)

    • Vector DB: Milvus collection for ISO/DIN standards
    • Embeddings: BERT-base on technical documentation
    • Retrieval: Top-k=5 relevant design patterns per query
    • Chunking Strategy: 512 tokens with 50-token overlap

  2. Task Graph Generation (agents/planner/task_decomposer.py)

    - NetworkX directed acyclic graph (DAG)
- Dependencies: geometric (parent/child), logical (sequence)
- JSON serialization for Generator consumption
- Cycle detection + topological sort validation

Generator Agent:

  1. PartDesign Workflow Enforcement (agents/generator/body_workflow.py)

    - Template library: Box, Cylinder, Loft, Sweep
- Constraint validation (Sketch closure, redundancy)
- Dynamic variable indexing (geo_id mapping)
- Attachment offset calculations

  2. Code Generation (agents/generator/llm_wrapper.py)

    • Model: CodeLlama-13B (4-bit quantization)
    • Prompting: Few-shot examples from curated dataset
    • Output Parsing: AST validation before execution
    • Safety: Whitelist FreeCAD API calls only

Testing:

  • 100 natural language prompts → executable scripts
  • Target: 70% success without refinement

Deliverables:

  • Planner retrieves relevant design standards
  • Generator produces valid PartDesign scripts
  • Task graph correctly sequences dependencies

Week 5-6: Validator & 3D Understanding

Validator Agent:

  1. Geometric Validation (agents/validator/geometry_checker.py)

    - OCC.Core.ShapeFix for manifold repair
- Tolerance checks (1e-6 default)
- Self-intersection detection (BRepAlgoAPI_Check)
- Volume/surface area sanity bounds

  2. FEA Integration (agents/validator/fea_runner.py)

    - Gmsh auto-meshing (tetrahedral, element size: auto)
- CalculiX static analysis
- Material library: Steel, Aluminum, ABS plastic
- Stress threshold warnings (von Mises > yield/2)

  3. Visual Critique (agents/validator/vision_validator.py)

    - STL export → rendered PNG (matplotlib/vtk)
- GPT-4V prompt: "Identify geometric anomalies"
- Confidence scoring on feedback

3D Machine Learning:

  1. Geometry Embeddings (ml/geometry_encoder.py)

    - Mesh → Point Cloud (Open3D, 2048 points)
- PointNet++ encoder (PyTorch)
- Output: 1024-dim vector per model
- Training: Contrastive learning on ShapeNet

  2. Feature Graph Embeddings (ml/graph_encoder.py)

    - B-Rep topology → NetworkX graph
- Node features: face area, edge curvature
- GraphSAGE (256-dim output)
- Use case: Similar design retrieval

Deliverables:

  • Validator catches 90%+ geometric errors
  • FEA runs automatically on generated parts
  • Point cloud embeddings cluster similar shapes

Phase 3: Scaling & Real-Time Collaboration (Weeks 7-10)

Week 7-8: Distributed Compute & Ray Integration

Ray Deployment:

  1. Agent Actors (core/distributed/ray_agents.py)

    @ray.remote
class PlannerActor:
    def __init__(self):
        # Load models/DBs in actor init

    async def plan(self, prompt: str) -> TaskGraph:
        # Stateful processing

  2. Kubernetes Configuration (k8s/ray-cluster.yaml)

    - Head node: 8 CPU, 16GB RAM
- Worker nodes: 4x (16 CPU, 32GB RAM each)
- Auto-scaling: 2-10 workers based on queue depth

  3. Resource Management

    - CPU-only for Planner/Orchestrator
- GPU (T4) for Generator/Validator LLMs
- Shared Redis for state synchronization

Testing:

  • 50 concurrent design tasks
  • Measure: latency (P95), throughput, resource utilization

Deliverables:

  • Ray cluster deployed on K8s
  • Agents scale horizontally under load
  • Shared state consistency verified

Week 9-10: Real-Time Sync & Dashboard

WebSocket Server (api/websocket_handler.py)

- FastAPI WebSocket endpoint
- Redis Pub/Sub bridge
- Events: state_update, validation_result, error
- Client: Three.js for 3D preview

Dashboard Features (frontend/dashboard/)

  1. 3D Viewer:

    • Three.js + STL loader
    • Real-time model updates via WebSocket
    • Camera controls, exploded views

  2. State Inspector:

    • Live DOM tree visualization
    • Feature history timeline
    • Validation logs stream

  3. Metrics:

    • Agent status (idle/busy)
    • Loop iteration count
    • Performance graphs (Plotly)

Optimization:

  1. Redis Caching (core/cache/primitive_cache.py)

    - Pre-compute common primitives (ISO bolts, gears)
- Cache key: parameter hash
- TTL: 24 hours
- Hit rate target: >60%

  2. Profiling (tools/performance/profiler.py)

    - cProfile integration
- Line-by-line timing (line_profiler)
- Memory snapshots (tracemalloc)
- Target: <5s per ReAct loop

Deliverables:

  • Real-time 3D preview in browser
  • Dashboard shows live agent activity
  • Performance optimized to <5s/loop

Phase 4: Production Hardening & Compliance (Weeks 11-12)

Week 11: Export, Audit, & GD&T Validation

Export Pipeline (core/export/cad_exporter.py)

- STEP AP214 (automotive standard)
- IGES 5.3 (legacy CAM compatibility)
- STL (high-resolution for 3D printing)
- Metadata injection: creation date, agent version

GD&T Validation (agents/validator/gdt_checker.py)

- Parse TechDraw annotations
- Validate: flatness, perpendicularity, concentricity
- Tolerance stack-up analysis
- Report: ISO 1101 compliance

Audit Trail (core/audit/stream_logger.py)

- Redis Streams for immutable logs
- Entry schema:
  {
    "timestamp": ISO8601,
    "agent": "generator",
    "action": "script_execution",
    "input_hash": sha256,
    "output_state": {...},
    "user_id": UUID
  }
- Retention: 90 days (compliance requirement)

Deliverables:

  • Multi-format export tested on 100 models
  • GD&T validation on technical drawings
  • Audit trail queryable via API

Week 12: Security, Load Testing, & Fine-Tuning

Security Hardening:

  1. Container Security (docker/Dockerfile.production)

    - Non-root user (freecad:1000)
- Read-only root filesystem
- Dropped capabilities (CAP_SYS_ADMIN)
- Network policies (K8s)

  2. Authentication (api/auth/oauth_handler.py)

    - OAuth 2.0 / OpenID Connect
- JWT tokens (15min expiry)
- Role-based access control (RBAC)
- API rate limiting (100 req/min per user)

Load Testing (tests/load/locust_scenarios.py)

- Tool: Locust
- Scenarios:
  1. 100 concurrent simple designs (boxes)
  2. 50 concurrent complex assemblies
  3. Spike test: 0→200 users in 1min
- Success Criteria:
  - P95 latency < 10s
  - Error rate < 1%
  - No memory leaks over 1hr

LLM Fine-Tuning:

  1. Dataset Curation (ml/training/dataset.py)

    - 1,000 expert-reviewed (prompt, script, model) triplets
- Sources: FreeCAD forum, GitHub, internal review
- Validation: 80/10/10 train/val/test split

  2. LoRA Training (ml/training/lora_finetuner.py)

    - Base model: Llama3-8B
- LoRA rank: 16, alpha: 32
- Training: 4x A100 GPUs, 24 hours
- Evaluation: BLEU score on code generation
- Target: >0.6 BLEU improvement

Deliverables:

  • OAuth authentication enforced
  • Load tests pass at 100 concurrent users
  • Fine-tuned model outperforms base by 30%+

4. Advanced Technical Specifications

4.1 Multimodal Embedding Strategy

Text Embeddings

Model: BERT-base-uncased
Input: FreeCAD XML + feature descriptions
Preprocessing: Tokenization → [CLS] token pooling
Dimensionality: 768
Use Case: Semantic design search

Geometric Embeddings

Model: PointNet++ / MeshCNN
Input: STL mesh → 2048 point cloud
Training: ShapeNet (55 categories)
Dimensionality: 1024
Use Case: Shape similarity, retrieval

Topological Embeddings

Model: GraphSAGE
Input: B-Rep feature graph
Node Features: [face_area, edge_curvature, vertex_valence]
Edge Features: [adjacency_type, angle]
Dimensionality: 256
Use Case: Structural pattern matching

4.2 Simulation Integration Pipeline

FreeCAD Model
    ↓
[Gmsh] → Tetrahedral Mesh (auto element size)
    ↓
[CalculiX INP File]
    ├─→ Material: E=200GPa, ν=0.3 (steel)
    ├─→ Boundary: Fixed faces detection
    └─→ Load: Pressure/force vectors
    ↓
[Static Analysis] → von Mises stress field
    ↓
[Topology Optimization] (PyMOO + SIMP)
    ↓
[Optimized Geometry] → Back to FreeCAD
Loading

Automated Failure Modes:

  • Max stress > 0.8 × Yield strength → Warning
  • Displacement > 10% of part size → Reject
  • Safety factor < 1.5 → Request design review

4.3 Geometry Repair Workflow

from OCC.Core.ShapeFix import ShapeFix_Shape

def auto_repair_geometry(shape):
    """
    Repair common CAD errors:
    - Non-manifold edges
    - Invalid face orientations
    - Small edges/faces (< tolerance)
    """
    fixer = ShapeFix_Shape(shape)
    fixer.SetPrecision(1e-6)
    fixer.SetMaxTolerance(1e-3)
    fixer.Perform()
    return fixer.Shape()

5. Deployment Architecture

5.1 Kubernetes Deployment

# k8s/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: freecad-orchestrator
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: orchestrator
        image: freecad-llm:v1.0
        resources:
          requests:
            cpu: "2"
            memory: "4Gi"
          limits:
            cpu: "4"
            memory: "8Gi"
        env:
        - name: REDIS_URL
          valueFrom:
            secretKeyRef:
              name: redis-secret
              key: url
---
# Ray cluster for distributed agents
apiVersion: ray.io/v1alpha1
kind: RayCluster
metadata:
  name: freecad-ray-cluster
spec:
  rayVersion: '2.9.0'
  headGroupSpec:
    serviceType: ClusterIP
    rayStartParams:
      dashboard-host: '0.0.0.0'
  workerGroupSpecs:
  - replicas: 4
    minReplicas: 2
    maxReplicas: 10
    rayStartParams:
      num-cpus: "16"

5.2 Monitoring Stack

Component Technology Metrics
Metrics Prometheus Agent latency, queue depth, error rate
Logging ELK Stack Structured JSON logs, full-text search
Tracing Jaeger Distributed request tracing
Alerting PagerDuty On-call rotation for critical failures

6. Testing Strategy

6.1 Test Pyramid

        ┌─────────────────┐
        │   E2E Tests     │  10 critical user journeys
        │   (Selenium)    │
        ├─────────────────┤
        │ Integration     │  50 agent interaction tests
        │ Tests (pytest)  │
        ├─────────────────┤
        │  Unit Tests     │  500+ function-level tests
        │  (pytest)       │  Target: 80% coverage
        └─────────────────┘

6.2 Validation Dataset

Complexity Tiers:

  1. Tier 1 (Simple): Primitives (box, cylinder, sphere) - 50 models
  2. Tier 2 (Moderate): Brackets, flanges, simple assemblies - 100 models
  3. Tier 3 (Complex): Gearboxes, engines, organic shapes - 50 models

Success Criteria:

  • Tier 1: 95% success rate
  • Tier 2: 85% success rate
  • Tier 3: 70% success rate

7. Risk Mitigation

Risk Probability Impact Mitigation
Topological naming errors High High Robust recompute handling, state rollback
LLM hallucination (invalid code) High Medium AST validation, sandbox execution
FEA solver instability Medium Medium Mesh quality checks, fallback to simpler analysis
K8s scaling delays Low Medium Pre-warmed worker pool, predictive scaling
Data privacy (model leakage) Low High On-premise deployment option, data encryption

8. Success Metrics (KPIs)

Performance

  • Iteration Speed: <5s per ReAct loop (P95)
  • Throughput: 100 concurrent designs
  • Uptime: 99.5% SLA

Quality

  • Success Rate: >85% on validation dataset
  • Geometric Accuracy: <0.1mm deviation from spec
  • FEA Validation: 90% of stress predictions within 15% of manual analysis

Business

  • User Adoption: 500 active users (6 months post-launch)
  • Time Savings: 60% reduction in CAD scripting time
  • Cost: <$0.50 per design iteration (compute cost)

9. Post-Launch Roadmap (6-12 Months)

  1. Assembly Intelligence: Multi-body constraint reasoning, kinematic simulation
  2. Generative Design: Lattice structures, topology optimization loop
  3. Sheet Metal Module: Unfold/bend sequence planning
  4. CAM Integration: Automatic toolpath generation for CNC
  5. Multi-CAD Support: SolidWorks/CATIA import via STEP translation
  6. Collaborative Editing: Operational transform for concurrent users

10. Resource Requirements

Development Team (12 weeks)

  • 2x Backend Engineers (Python, Ray, K8s)
  • 1x ML Engineer (PyTorch, NLP)
  • 1x CAD Domain Expert (FreeCAD, mechanical engineering)
  • 1x DevOps Engineer (Docker, CI/CD)
  • 1x QA Engineer (Test automation)

Infrastructure (Production)

  • Compute: 4x GPU nodes (T4), 8x CPU nodes (16 cores each)
  • Storage: 500GB SSD (Redis), 2TB HDD (logs, models)
  • Bandwidth: 10Gbps internal, 1Gbps external

Budget Estimate

  • Development: $300k (salaries, 12 weeks)
  • Infrastructure: $2k/month (cloud compute)
  • LLM API Costs: $500/month (GPT-4V + fine-tuning)
  • Total Year 1: ~$350k

Appendix A: Code Examples

A.1 LangGraph State Machine

from langgraph.graph import StateGraph, END
from typing import TypedDict

class DesignState(TypedDict):
    prompt: str
    task_graph: dict
    script: str
    model_state: dict
    validation_result: dict
    iteration: int

def build_workflow():
    workflow = StateGraph(DesignState)

    workflow.add_node("planner", planner_agent)
    workflow.add_node("generator", generator_agent)
    workflow.add_node("executor", freecad_executor)
    workflow.add_node("validator", validator_agent)

    workflow.set_entry_point("planner")
    workflow.add_edge("planner", "generator")
    workflow.add_edge("generator", "executor")
    workflow.add_edge("executor", "validator")

    workflow.add_conditional_edges(
        "validator",
        should_continue,
        {
            "refine": "generator",
            "success": END,
            "human_review": "human_node"
        }
    )

    return workflow.compile()

A.2 Ray Distributed Execution

import ray

@ray.remote(num_cpus=2, num_gpus=0.5)
class GeneratorActor:
    def __init__(self):
        self.model = load_codellama()

    async def generate(self, task_graph):
        script = await self.model.generate(task_graph)
        return validate_and_clean(script)

# Parallel execution
actors = [GeneratorActor.remote() for _ in range(4)]
results = ray.get([
    actor.generate.remote(task)
    for actor, task in zip(actors, task_batch)
])

Appendix B: Configuration Files

B.1 Docker Compose (Development)

version: '3.8'

services:
  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
    volumes:
      - redis_data:/data

  freecad:
    build:
      context: .
      dockerfile: docker/Dockerfile.freecad
    environment:
      - DISPLAY=:99
      - REDIS_URL=redis://redis:6379
    volumes:
      - ./outputs:/app/outputs
    depends_on:
      - redis

  orchestrator:
    build:
      context: .
      dockerfile: docker/Dockerfile.app
    ports:
      - "8000:8000"
    environment:
      - REDIS_URL=redis://redis:6379
      - OPENAI_API_KEY=${OPENAI_API_KEY}
    depends_on:
      - redis
      - freecad

volumes:
  redis_data:

Document Version

  • Version: 1.0
  • Date: January 1, 2026
  • Authors: AI Design Engineering Team
  • Status: Implementation Ready

Next Steps

  1. Week 0 (Pre-Implementation):

    • Secure infrastructure budget approval
    • Finalize team assignments
    • Set up development environments
    • Kickoff meeting with stakeholders

  2. Week 1 (Day 1):

    • Create feature branches
    • Initialize CI/CD pipelines
    • First standup meeting
    • Begin headless FreeCAD refactoring

Let's build the future of AI-assisted CAD design! 🚀